Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has rapidly emerged as an effective treatment in medically refractory Parkinson's disease (PD). However, our understanding of the effects of DBS is limited and as a result programming DBS devices for optimal clinical benefit is a difficult and time consuming process. The central hypothesis of the planned work is that patient-specific models of DBS can predict a theoretically optimal stimulation parameter setting that will provide therapeutic benefit equal to that achieved by current trial-and-error programming strategies. The fundamental concept behind this project is that if clinicians had tools that enabled visualization of the anatomical and electrical effects of DBS they would be able to quickly and accurately adjust the stimulation for maximal therapeutic benefit. Previous work of the principal investigator and his collaborators has provided the core scientific components necessary to realize such a tool. However, no quantitative measures of the size and shape of the 3D volume of tissue activated by DBS currently exist within the clinical arena. Therefore, we propose the development of patient-specific models of STN DBS based on anatomical and diffusion tensor magnetic resonance imaging (MRI). We will use these models to establish correlations between electrode locations / stimulation parameters / volumes of activation and therapeutic benefit as determined from clinical evaluation of individual patients. We will then use the models to define theoretically optimal stimulation parameter settings custom to the individual. The therapeutic efficacy of the model-designed settings will then be compared to the settings determined by current clinical practice. If our hypothesis is supported, we believe the technology and software developed in this study could significantly decrease the time and effort necessary to program DBS devices, and be applicable to a wide range of clinical applications including PD, essential tremor, dystonia, epilepsy, and obsessive-compulsive disorder.